Bias-corrected Kullback–Leibler distance criterion based model selection with covariables missing at random

Yuting Wei, Qihua Wang, Xiaogang Duan and Jing Qin

Computational Statistics & Data Analysis, 2021, vol. 160, issue C

Abstract: A model selection problem for the conditional probability function of the response variable Y given the covariable vector (X,Z) is considered under the case where X is missing at random. And two novel model selection criteria are suggested. It is shown that the model selection by these two criteria is consistent and that the population parameter estimators, corresponding to the selected model, are also consistent and asymptotically normal. Extensive simulation studies are conducted to investigate the finite-sample performances of the proposed two criteria and a thorough comparison is made with some related model selection strategies. Moreover, two real data analyses are presented for illustrating the practical application of the proposed two criteria.

Keywords: Pseudo empirical likelihood; Missing covariates; Bias correction (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:160:y:2021:i:c:s016794732100058x

DOI: 10.1016/j.csda.2021.107224

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